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Review Swarm

CommunityPopular
Dimillian
review-swarm

Parallel read-only multi-agent review of a current git diff or explicit file scope to find behavioral regressions, security or privacy risks, performance or reliability issues, and contract or test coverage gaps. Use when the user asks for a review swarm, parallel review, diff review, regression review, security review, or wants high-signal issues plus a prioritized fix path without editing files.

Overview

PublisherDimillian
RepositorySkills
Skill namereview-swarm
Stars
4K
Forks
206
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Dimillian on GitHub. Read the source before you install it.

Installation

Install the Review Swarm AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Dimillian/Skills.git /tmp/Skills
mkdir -p .claude/skills
cp -r /tmp/Skills/review-swarm .claude/skills/review-swarm
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Swarm in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Review Swarm on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Review Swarm is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Review Swarm

Review a diff with four read-only sub-agents in parallel, then have the main agent filter, order, and summarize only the issues that matter. This skill is review-only: sub-agents do not edit files, and the main agent does not apply fixes as part of this workflow.

Step 1: Determine Scope and Intent

Prefer this scope order:

  1. Files or paths explicitly named by the user
  2. Current git changes
  3. An explicit branch, commit, or PR diff requested by the user
  4. Most recently modified tracked files, only if the user asked for a review and there is no clearer diff

If there is no clear review scope, stop and say so briefly.

When using git changes, choose the smallest correct diff command:

  • unstaged work: git diff
  • staged work: git diff --cached
  • mixed staged and unstaged work: review both
  • explicit branch or commit comparison: use exactly what the user requested

Before launching reviewers, read the closest local instructions and any relevant project docs for the touched area, such as:

  • AGENTS.md
  • repo workflow docs
  • architecture or contract docs for the touched module

Build a short intent packet for the reviewers:

  1. What behavior is meant to change
  2. What behavior should remain unchanged
  3. Any stated or inferred constraints, such as compatibility, rollout, security, or migration expectations

If the user did not state the intent clearly, infer it from the diff and say that the inference may be incomplete.

Step 2: Launch Four Read-Only Reviewers in Parallel

Launch four sub-agents when the scope is large enough for parallel review to help. For a tiny diff or one very small file, it is acceptable to review locally instead.

For every sub-agent:

  • give the same scope and the same intent packet
  • state that the sub-agent is read-only
  • do not let the sub-agent edit files, run apply_patch, stage changes, commit, or perform any other state-mutating action
  • ask for concise findings only
  • ask for: file and line or symbol, issue, why it matters, recommended follow-up, and confidence
  • tell the sub-agent to avoid nits, style preferences, and speculative concerns without concrete impact
  • tell the sub-agent to send findings back to the main agent only

Use these four review roles.

Sub-Agent 1: Intent and Regression Review

Review whether the diff matches the intended behavior change without introducing extra behavior drift.

Check for:

  1. Unintended behavior changes outside the stated scope
  2. Broken edge cases or fallback paths
  3. Contract drift between callers and callees
  4. Missing updates to adjacent flows that should change together

This sub-agent is read-only. It must not edit files, apply patches, or make any other workspace changes.

Recommended sub-agent role: reviewer

Sub-Agent 2: Security and Privacy Review

Review the diff for security regressions, privacy risks, and trust-boundary mistakes.

Check for:

  1. Missing or weakened authn or authz checks
  2. Unsafe input handling, injection risks, or validation gaps
  3. Secret, token, or sensitive data exposure
  4. Risky defaults, permission expansion, or trust of unverified data

This sub-agent is read-only. It must not edit files, apply patches, or make any other workspace changes.

Recommended sub-agent role: reviewer

Sub-Agent 3: Performance and Reliability Review

Review the diff for new cost, fragility, or operational risk.

Check for:

  1. Duplicate work, redundant I/O, or unnecessary recomputation
  2. Added work on startup, render, request, or other hot paths
  3. Leaks, missing cleanup, retry storms, or subscription drift
  4. Ordering, race, or failure-handling problems that make the change brittle

This sub-agent is read-only. It must not edit files, apply patches, or make any other workspace changes.

Recommended sub-agent role: reviewer

Sub-Agent 4: Contracts and Coverage Review

Review the diff for compatibility gaps and missing safety nets.

Check for:

  1. API, schema, type, config, or feature-flag mismatches
  2. Migration or backward-compatibility fallout
  3. Missing or weak tests for the changed behavior
  4. Missing logs, metrics, assertions, or error paths that make regressions harder to detect

This sub-agent is read-only. It must not edit files, apply patches, or make any other workspace changes.

Recommended sub-agent role: reviewer

Report only issues that materially affect correctness, security, privacy, reliability, compatibility, or confidence in the change. It is better to miss a nit than to bury the user in low-value noise.

Step 3: Aggregate and Filter Findings

The main agent owns synthesis. Treat sub-agent output as raw review input, not final output.

Merge findings across all four reviewers and filter aggressively:

  • drop duplicates
  • drop weak or speculative claims
  • drop issues that conflict with the stated intent
  • drop minor style or readability comments unless they hide a real bug or maintenance risk

Normalize surviving findings into this shape:

  1. File and line or nearest symbol
  2. Category: regression, security, reliability, or contracts
  3. Severity: high, medium, or low
  4. Why it matters
  5. Recommended fix or follow-up
  6. Confidence: high, medium, or low

If a reviewer may be correct but the intent is unclear, turn it into an open question instead of a finding.

Step 4: Order the Output

Present findings in this order:

  1. High-severity, high-confidence issues
  2. Medium-severity issues that are likely worth fixing before merge
  3. Lower-severity issues or follow-ups that can wait

Keep the review concise. Findings should be actionable and evidence-backed.

If there are no material issues, say that directly instead of manufacturing feedback.

Step 5: Recommend a Clear Path Forward

After the findings, give the user a short path forward:

  • what to fix before merge
  • what to improve if time permits
  • what can safely be left alone

When helpful, group the path forward into:

  • fix now
  • fix soon
  • optional follow-up

Do not implement fixes as part of this skill. The output is a read-only review plus a prioritized recommendation.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Review Swarm AI skill do?

Parallel read-only multi-agent review of a current git diff or explicit file scope to find behavioral regressions, security or privacy risks, performance or reliability issues, and contract or test coverage gaps. Use when the user asks for a review swarm, parallel review, diff review, regression review, security review, or wants high-signal issues plus a prioritized fix path without editing files.

Why use Review Swarm on TypingMind?

Because you install it once and use it with any model. Review Swarm is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Review Swarm in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dimillian/Skills/tree/main/review-swarm. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Review Swarm?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Review Swarm?

As many as you like. As long as a model supports skills, you can use Review Swarm with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Review Swarm AI skill free?

Yes. It is published on GitHub by Dimillian under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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